Machine Learning with Flood Prediction

: Floods are the most common natural disasters that are highly complex to model. They can cause destruction and devastation of natural life, agriculture, infrastructure and properties, every year. There are many researches on the advancement of flood prediction models. These models have contributed well to risk reduction, minimisation of the loss of human life and reduction of property damage associated with flood. Here, we use Machine Learning models for prediction. ML methods provide better performance and costeffective solutions. Some of the ML algorithms which were reported as effective for both short-term and longterm flood forecasts are Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Support Vector Regression (SVR). Here, in this report we introduce the most promising prediction method for both short-term and long-term floods. Flood prediction is one of the most challenging and difficult problems in hydrology. Flood disaster had great impact on city development like reduction in economic condition and life loses. The aim of this is to discovering more accurate and efficient prediction model. The main contribution of this is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. Early Flood Warning System (EFWS) are promising curative against flood hazards and losses. Machine learning is the centerpiece for building a satisfactory early flood warning system. 1. INTRODUCTION Flood Prediction is one of the most challenging and difficult problems in hydrology. Flood disaster had great impact on city development like reduction in economic condition and life losses. In many regions the flood forecasting technique is the reliable technique for more accurate flood prediction. The flood formation system using ML algorithm is a convenient tool for managing floods in the integrating analytics and also machine learning algorithms for more dynamic flood risk visualization and prediction. The aim is discovering more accurate and efficient prediction models. The main contribution of this is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. Early Flood Warning System (EFWS) are promising curative against flood hazards and losses. Machine learning is the centerpiece for building a satisfactory early flood warning system. This summarizes the machine learning methods proposed in these special issues for flood forecasts and prediction with their proper advantages. The changing patterns and behaviors of river water levels that may lead to flooding are an interesting and practical research area. They are configured to mitigate economic and societal implications brought about by floods. Support Vector Machine (SVM) are machine learning algorithms suitable for predicting